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Chronos-2: Ensure base model revision is saved in LoRA adapter config #432

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Chronos-2: Ensure base model revision is saved in LoRA adapter config #432

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53bc613
Chronos-2: Ensure base model revision is saved in LoRA adapter config
ryuichi-ichinose Dec 11, 2025
7f2f5a1
Refactor revision handling: remove private attribute hack
ryuichi-ichinose Dec 12, 2025
6248da8
Add: If dict is given
ryuichi-ichinose Dec 13, 2025
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Refactor revision handling: remove private attribute hack
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@ryuichi-ichinose
ryuichi-ichinose committed Dec 12, 2025
commit 7f2f5a1d18aa307e521f8c11fb4d2ef083d5c6d5
19 changes: 5 additions & 14 deletions src/chronos/chronos2/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,10 +40,10 @@ class Chronos2Pipeline(BaseChronosPipeline):
forecast_type: ForecastType = ForecastType.QUANTILES
default_context_length: int = 2048

def __init__(self, model: Chronos2Model):
def __init__(self, model: Chronos2Model, revision: str | None = None):
super().__init__(inner_model=model)
self.model = model

self.revision = revision
@staticmethod
def _get_prob_mass_per_quantile_level(quantile_levels: torch.Tensor) -> torch.Tensor:
"""
Expand Down Expand Up @@ -195,7 +195,7 @@ def fit(
model.load_state_dict(self.model.state_dict())

if finetune_mode == "lora":
lora_revision = getattr(self.model.config, "_source_revision", None)
lora_revision = self.revision
if lora_config is None:
lora_config = LoraConfig(
r=8,
Expand All @@ -210,7 +210,6 @@ def fit(
revision=lora_revision,
)
elif isinstance(lora_config, dict):
lora_config.setdefault("revision", lora_revision)
lora_config = LoraConfig(**lora_config)
else:
assert isinstance(lora_config, LoraConfig), (
Expand Down Expand Up @@ -1182,11 +1181,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):

model = AutoPeftModel.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
model = model.merge_and_unload()

if revision:
model.config._source_revision = revision

return cls(model=model)
return cls(model=model, revision=revision)

# Handle the case for the base model
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
Expand All @@ -1200,11 +1195,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
class_ = Chronos2Model

model = class_.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)

if revision:
model.config._source_revision = revision

return cls(model=model)
return cls(model=model, revision=revision)

def save_pretrained(self, save_directory: str | Path, *args, **kwargs):
"""
Expand Down
11 changes: 3 additions & 8 deletions test/test_chronos2.py
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Expand Up @@ -1139,18 +1139,13 @@ def test_lora_config_uses_source_revision_from_instantiation(
pipeline: Chronos2Pipeline, tmpdir, source_revision
):
"""
Test that fit in 'lora' mode correctly uses the '_source_revision'
that was (notionally) stored in the model's config during instantiation.
Test that fit in 'lora' mode correctly uses the 'revision'
stored in the pipeline instance.
"""
output_dir = Path(tmpdir)
dummy_inputs = [torch.rand(100)]

# Manually set the source revision on the config to simulate
# it being set during from_pretrained
if source_revision:
pipeline.model.config._source_revision = source_revision
elif hasattr(pipeline.model.config, "_source_revision"):
delattr(pipeline.model.config, "_source_revision")
pipeline.revision = source_revision

pipeline.fit(
inputs=dummy_inputs,
Expand Down